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Use an upper and a lower timewise quantile series to highlight the possible outliers in a collection of time series.
outlier.plot(x, prob = 0.05, percent = 0.05, xtime = NULL)
T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns.
Tail probability. That is, the two quantile series is (prob, 1-prob). prob is restricted to be in (0,0.15). Default value is 0.05.
The number of possible outliers in each side is T*k*prob*percent.
A vector with the values for the x labels. Default values are 1, 2, 3, ...
A list containing:
standardized - A matrix containing standardized time series.
qts - The timewise quantile of order prob.
minseries - The timewise minimum of the standardized time series.
maxseries - The timewise maximum of the standardized time series.
# NOT RUN { data(TaiwanAirBox032017) output <- outlier.plot(TaiwanAirBox032017[,1:3]) # }
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